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Related Experiment Video

Updated: Mar 25, 2026

Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels
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High-fidelity 'top-down' DLP bioprinting of multi-material soft tissue constructs enabled by computer vision-based

Nadina Aimé Usseglio1, Alejandro González-Santos1,2, Alba Fernandez Ferrer1

  • 1Advanced Biofabrication Laboratory-DNIETO LAB, Centro Interdisciplinar de Química e Bioloxía, CICA, Universidade da Coruña. A Coruña, Spain, Spain.

Biofabrication
|March 23, 2026
PubMed
Summary

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A Novel Sprayable Fibrinogen/Glycosaminoglycans/Collagen-Based Bioink for Skin Wound Healing Applied by a Handheld Dual-Head Airbrush.

Advanced healthcare materials·2025
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Biofabrication approaches and regulatory framework of metastatic tumor-on-a-chip models for precision oncology.

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Multi-material digital light processing bioprinting of hydrogel-based microfluidic chips.

Biofabrication·2021
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Fundamentals of light-cell-polymer interactions in photo-cross-linking based bioprinting.

APL bioengineering·2020

This study introduces an automated platform for precise layer thickness control in multi-material bioprinting, enhancing tissue engineering. The vision-based system achieves sub-0.1 mm accuracy, improving reproducibility and scalability for regenerative medicine applications.

Area of Science:

  • Regenerative Medicine
  • Bioprinting Technologies
  • Tissue Engineering

Background:

  • Bioprinting enables complex 3D tissue fabrication but faces challenges in multi-material integration and structural fidelity.
  • Precise layer thickness control is critical in top-down vat photopolymerization bioprinting, particularly Digital Light Processing (DLP), affecting mechanical integrity and resolution.

Purpose of the Study:

  • To develop and validate a novel automated platform for real-time measurement of the dynamic gap in multi-material top-down DLP bioprinting.
  • To overcome limitations in precise layer thickness control, a key bottleneck in functional tissue engineering.

Main Methods:

  • Comparative assessment of classical computer vision and deep learning (CNN-based) techniques for dynamic gap measurement.
  • Development of a vision-based system for automated, real-time layer thickness monitoring during bioprinting.
Keywords:
3D bioprintingartificial intelligencecomputer visiondigital light processingmultimaterial bioprintingvat photopolymerization

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  • Testing system adaptability across various bioinks with different viscosities, opacities, and photopolymerization kinetics.

    • Main Results:

    • Achieved sub-0.1 mm precision (0.092 mm) in layer thickness measurement with high correlation to mechanical measurements (R = 0.994).
    • Demonstrated adaptability to diverse bioinks without manual recalibration.
    • Successfully bioprinted multi-material vascular-like and skin tissue models with high spatial fidelity and compartmentalization.

    Conclusions:

    • The developed vision-based platform significantly improves reproducibility, material adaptability, and structural precision in multi-material bioprinting.
    • This advancement addresses critical bottlenecks, paving the way for clinically scalable tissue manufacturing systems.
    • The system enhances biofabrication workflows for creating functional tissue constructs for regenerative medicine.